06: MMLN: An R Package for Mixed-Effects Multinomial Regression and Model Diagnostics

Music City Center 

Multinomial outcomes arise in numerous fields---from sports and species counts to genomics---yet existing software often focuses on simpler fixed-effects or purely multinomial (logistic) frameworks. The MMLN package introduces a suite of functions to fit more complex multinomial regression models, including incorporation of random effects, and evaluate the fit of all multinomial regression models using the squared Mahalanobis distance residuals proposed by Gerber and Craig (2023). These residuals generalize the randomized quantile residuals of Dunn and Smyth (1996) beyond the binomial case. The MMLN() function fits mixed-effects multinomial logistic-normal models via MCMC sampling, while the MDRes() function computes the squared Mahalanobis distance residuals to comprehensively evaluate model adequacy. Users can visualize or formally test these using quantile-quantile plots and Kolmogorov-Smirnov tests. The MMLN package broadens the practical toolbox for analyzing multinomial data by integrating flexible modeling with robust diagnostic tools.